The embodiments herein relate to using a question-answering system to support a human expert in problem solving in a particular domain, and more specifically to a decision-support application and system for problem solving using a question-answering system.
Decision-support systems exist in many different industries where human experts require assistance in retrieving and analyzing information. An example that will be used throughout this application is a diagnosis system employed in the health care industry.
Diagnosis systems can be classified into systems that use structured knowledge, systems that use unstructured knowledge, and systems that use clinical decision formulas, rules, trees, or algorithms. The earliest diagnosis systems used structured knowledge or classical, manually constructed knowledge bases. The Internist-I system developed in the 1970s uses disease-finding relations and disease-disease relations, with associated numbers such as sensitivity, the fraction of patients with a disease who have finding (Myers, J. D. The background of INTERNIST-I and QMR. In Proceedings of ACM Conference on History of Medical Informatics (1987), 195-197).
The MYCIN system for diagnosing infectious diseases, also developed in the 1970s, uses structured knowledge in the form of production rules, stating that if certain facts are true, then one can conclude certain other facts with a given certainty factor (Buchanan, B. G. and Shortliffe, E. H. (Eds.) Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Addison-Wesley, Reading, Mass., 1984). DXplain, developed starting in the 1980s, uses structured knowledge similar to that of Internist-I, but adds a hierarchical lexicon of findings (Barnett, G. O., Cimino, J. J., Hupp, J. A., Hoffer, E. P. DXplain: An evolving diagnostic decision-support system. JAMA 258, 1 (1987), 67-74).
Iliad, developed starting in the 1990s, adds more sophisticated probabilistic reasoning. Each disease has an associated a priori probability of the disease (in the population for which Iliad was designed), and a list of findings, along with the fraction of patients with the disease who have the finding (sensitivity), and the fraction of patients without the disease who have the finding (1—specificity) (Warner, H. R., Haug, P., Bouhaddou, O., Lincoln, M., Warner, H., Sorenson, D., Williamson, J. W. and Fan, C. ILIAD as an expert consultant to teach differential diagnosis. In Proc. Annu. Symp. Comput. Appl. Med. Care. (1988), 371-376). DiagnosisPro (http://en.diagnosispro.com) is a structured knowledge base that can be queried and browsed online.
In 2000, diagnosis systems using unstructured knowledge started to appear. These systems use some structuring of knowledge. For example, entities such as findings and disorders may be tagged in documents to facilitate retrieval. ISABEL uses Autonomy information retrieval software and a database of medical textbooks to retrieve appropriate diagnoses given input findings (Ramnarayan, P., Tomlinson, A., Rao, A., Coren, M., Winrow, A. and Britto, J. ISABEL: A web-based differential diagnostic aid for paediatrics: Results from an initial performance evaluation. Archives of Disease in Childhood 88, 5 (2003), 408-413).
Autonomy Auminence uses the Autonomy technology to retrieve diagnoses given findings and organizes the diagnoses by body system (http://www.autonomyhealth.com). First CONSULT allows one to search a large collection of medical books, journals, and guidelines by chief complaints and age group to arrive at possible diagnoses (http://www.firstconsult.com). PEPID DDX is a diagnosis generator based on PEPID's independent clinical content (http://www.pepid.com/products/ddx/).
Clinical decision rules have been developed for a number of disorders, and computer systems have been developed to help practitioners and patients apply these rules. The Acute Cardiac Ischemia Time-Insensitive Predictive Instrument (ACI-TIPI) takes clinical and ECG features as input and produces probability of acute cardiac ischemia as output (Selker, H. P., Beshansky, J. R., Griffith, J. L., Aufderheide, T. P., Ballin, D. S., Bernard, S. A., Crespo, S. G., Feldman, J. A., Fish, S. S., Gibler, W. B., Kiez, D. A., McNutt, R. A., Moulton, A. W., Ornato, J. P., Podrid, P. J., Pope, J. H., Salem, D. N., Sayre, M. R. and Woolard, R. H. Use of the acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI) to assist with triage of patients with chest pain or other symptoms suggestive of acute cardiac ischemia: A multicenter, controlled clinical trial. Annals of Internal Medicine 129, 11 (1998), 845-855). For example, ACI-TIPI is incorporated into commercial heart monitors/defibrillators.
The CaseWalker system uses a four-item questionnaire to diagnose major depressive disorder (Cannon, D. S. and Allen, S. N. A comparison of the effects of computer and manual reminders on compliance with a mental health clinical practice guideline. Journal of the American Medical Informatics Association 7, 2 (2000), 196-203). The PKC Advisor provides guidance on 98 patient problems such as abdominal pain and vomiting (http://www.pkc.com/software/advisor/).
The strengths of current diagnosis systems are that they can improve clinicians' diagnostic hypotheses (Friedman, C. P., Elstein, A. S., Wolf, F. M., Murphy, G. C., Franz, T. M., Heckerling, P. S., Fine, P. L., Miller, T. M. and Abraham, V. Enhancement of clinicians' diagnostic reasoning by computer-based consultation: A multisite study of 2 systems. JAMA 282, 19 (1999), 1851-1856), and can help clinicians avoid missing important diagnoses (Ramnarayan, P., Roberts, G. C., Coren, M., Nanduri, V., Tomlinson, A., Taylor, P. M., Wyatt, J. C. and Britto, J. F. Assessment of the potential impact of a reminder system on the reduction of diagnostic errors: A quasi-experimental study. BMC Med. Inform. Decis. Mak. 6, 22 (2006)).
Current diagnosis systems are not widely used (Berner, E. S. Diagnostic Decision Support Systems Why aren't they used more and what can we do about it? AMIA Annu. Symp. Proc. 2006 (2006), 1167-1168, hereinafter referred to as Berner, 2006) because the systems suffer from limitations that prevent them from being integrated into the day-to-day operations of health organizations (Coiera, E. Guide to Health Informatics (Second Edition). Hodder Arnold, 2003; and Shortliffe, T. Medical thinking: What should we do? In Proceedings of Medical Thinking: What Do We Know? A Review Meeting (2006), http://www.openclinical.org/medicalThinking2006Summary2.html, hereinafter referred to as Shortliffe, 2006).
Many different healthcare workers may see a patient, and patient data may be scattered across many different computer systems in both structured and unstructured form. Also, the systems are difficult to interact with (Berner, 2006; Shortliffe, 2006). The entry of patient data is difficult, the list of diagnostic suggestions may be too long, and the reasoning behind diagnostic suggestions is not always transparent. Further, the systems are not focused enough on next actions, and do not help the clinician figure out what to do to help the patient (Shortliffe, 2006). The systems are also unable to ask the practitioner for missing information that would increase confidence in a diagnosis, and they are not always based on the latest, high-quality medical evidence and have difficulty staying up-to-date (Sim, I., Gorman, P., Greenes, R. A., Haynes, R. B., Kaplan, B., Lehmann, H. and Tang, P. C. Clinical decision support systems for the practice of evidence-based medicine. J. Am. Med. Inform. Assoc. 8, 6 (2001), 527-534).
In view of these issues, the disclosed embodiments herein provide an improved medical diagnosis system.